The phenotype associated with the Mendelian disease autosomal dominant polycystic kidney disease (ADPKD) is highly variable. Of paramount importance clinically is the severity of the renal cystic disease. Previous studies have shown that genic (PKD1 or PKD2) and (to a less extent) allelic factors influence the phenotype, but one of the most important modulating factors is genetic background. Identifying quantitative trait loci (QTL) that significantly influence the severity of disease would help understand pathogenesis, be of prognostic importance and may guide therapeutics. The development of high-density SNP arrays provides a means to map these QTL in large, clinically and genetically well-characterized populations, employing a genome-wide association study (GWAS). The NIDDK-funded CRISP and HALT PKD studies have a combined cohort of >1,000 ADPKD patients who are highly characterized clinically and genetically; >700 have renal MR imaging data. MR calculated total kidney volume (TKV) has been shown to be a good measure of disease severity, informative before a decline in GFR is detectable. Additionally, well characterized PKD1 populations are available at UCHSC, Emory, Mayo, KUMC, Toronto, Cambridge and Oxford to aid the discovery, replication and verification steps that are required to differentiate genuine QTL from false positive associations. Here, we propose a GWAS employing the Illumina Human660w-quad Genotyping BeadChip (658,000 SNPs), in 1600 PKD1 Caucasians (Aim 1). Total kidney volumes (TKV) data will be available in ~1100 of these patients and informative eGFR data in ~900 cases. The 7600 most likely associated SNPs detected with these endpoints will be further assayed using a customized array in a replicate population of 1600 PKD1 patients with the TKV and eGFR phenotypic endpoints, as above (Aim 2). A final verification step will analyze the 30 most promising loci in a population of 1216 patients with the same endpoints (Aim 3). The 30 loci will each be tested with ~12 SNPs (total of 348) to refine the QTL and highlight possible causative genes. To maximize the power of the study we will analyze the data as a combined population and family-based association study. In summary, this consortium of groups will perform the first GWAS in ADPKD to identify modifiers of disease severity. A three-stage design; discovery, replication and verification steps are to be employed to maximize the chance to identify QTL and minimize false positives.